Robotics: Science and Systems V

Accurate rough terrain estimation with space-carving kernels

R. Hadsell, J. A. Bagnell, D. Huber and M. Hebert

Abstract:

Accurate terrain estimation is critical for autonomous
offroad navigation. Reconstruction of a 3D surface
allows rough and hilly ground to be represented, yielding faster
driving and better planning and control. However, data from
a 3D sensor samples the terrain unevenly, quickly becoming
sparse at longer ranges and containing large voids because
of occlusions and inclines. The proposed approach uses online
kernel-based learning to estimate a continuous surface over the
area of interest while providing upper and lower bounds on
that surface. Unlike other approaches, visibility information is
exploited to constrain the terrain surface and increase precision,
and an efficient gradient-based optimization allows for realtime
implementation.